Query Expansion Based on Clustered Results

TL;DR

This paper introduces a novel framework for query expansion that clusters search results to generate more accurate expanded queries, especially for ambiguous or exploratory queries, addressing limitations of traditional keyword expansion methods.

Contribution

It formalizes the clustered result-based query expansion problem, proves its APX-hardness, and proposes two efficient algorithms for generating improved expanded queries.

Findings

The proposed algorithms effectively generate relevant expanded queries.

Clustering results improves handling of ambiguous and exploratory queries.

The problem is formally shown to be APX-hard.

Abstract

Query expansion is a functionality of search engines that suggests a set of related queries for a user-issued keyword query. Typical corpus-driven keyword query expansion approaches return popular words in the results as expanded queries. Using these approaches, the expanded queries may correspond to a subset of possible query semantics, and thus miss relevant results. To handle ambiguous queries and exploratory queries, whose result relevance is difficult to judge, we propose a new framework for keyword query expansion: we start with clustering the results according to user specified granularity, and then generate expanded queries, such that one expanded query is generated for each cluster whose result set should ideally be the corresponding cluster. We formalize this problem and show its APX-hardness. Then we propose two efficient algorithms named iterative single-keyword refinement and partial elimination based convergence, respectively, which effectively generate a set of expanded queries from clustered results that provide a classification of the original query results. We believe our study of generating an optimal query based on the ground truth of the query results not only has applications in query expansion, but has significance for studying keyword search quality in general.